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Nonparametric predictive inference for combined competing risks data

Coolen-Maturi, T.; Coolen, F.P.A.

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The nonparametric predictive inference (NPI) approach for competing risks data has recently been presented, in particular addressing the question due to which of the competing risks the next unit will fail, and also considering the effects of unobserved, re-defined, unknown or removed competing risks. In this paper, we introduce how the NPI approach can be used to deal with situations where units are not all at risk from all competing risks. This may typically occur if one combines information from multiple samples, which can, e.g. be related to further aspects of units that define the samples or groups to which the units belong or to different applications where the circumstances under which the units operate can vary. We study the effect of combining the additional information from these multiple samples, so effectively borrowing information on specific competing risks from other units, on the inferences. Such combination of information can be relevant to competing risks scenarios in a variety of application areas, including engineering and medical studies.


Coolen-Maturi, T., & Coolen, F. (2014). Nonparametric predictive inference for combined competing risks data. Reliability Engineering & System Safety, 126, 87-97.

Journal Article Type Article
Acceptance Date Jan 12, 2014
Online Publication Date Jan 24, 2014
Publication Date Jun 1, 2014
Deposit Date Feb 8, 2014
Publicly Available Date Jun 4, 2014
Journal Reliability Engineering and System Safety
Print ISSN 0951-8320
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 126
Pages 87-97
Keywords Imprecise probability, Lower and upper probability, Nonparametric predictive inference, Competing risks, Right-censored data, Combined data.


Accepted Journal Article (336 Kb)

Copyright Statement
NOTICE: this is the author’s version of a work that was accepted for publication in Reliability Engineering & System Safety. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Reliability Engineering & System Safety, 126, 2014, 10.1016/j.ress.2014.01.007.

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